标题： 基于签名增强变压器的VWAP执行：多资产学习方法 显示英文标题

标题： VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach

摘要： 本文提出了一种针对交易量加权平均价格（VWAP）执行的新方法，解决了两个关键的实际挑战：资产特定模型训练的需求以及捕捉复杂的时间依赖关系的能力。基于我在动态VWAP执行方面的最新研究（arXiv:2502.18177），我展示了通过单一神经网络在多个资产上进行训练可以达到与传统资产特定模型相当甚至更好的性能。所提出的架构结合了受arXiv:2406.02486启发的基于变压器的设计和路径签名，用于捕获价格-成交量轨迹的几何特征，如arXiv:2406.17890所示。通过对来自80个交易对的小时级加密货币交易数据的经验分析表明，具有签名特征的全局拟合模型（GFT-Sig）在绝对和二次VWAP损失指标方面优于资产特定方法。值得注意的是，这些改进对于样本外资产仍然存在，显示了该模型在不同市场条件下的泛化能力。结果表明，结合全局参数共享与基于签名的特征提取提供了一种可扩展且稳健的VWAP执行方法，在实践中显著优于传统的资产特定实现方式。 显示英文摘要

摘要： In this paper I propose a novel approach to Volume Weighted Average Price (VWAP) execution that addresses two key practical challenges: the need for asset-specific model training and the capture of complex temporal dependencies. Building upon my recent work in dynamic VWAP execution arXiv:2502.18177, I demonstrate that a single neural network trained across multiple assets can achieve performance comparable to or better than traditional asset-specific models. The proposed architecture combines a transformer-based design inspired by arXiv:2406.02486 with path signatures for capturing geometric features of price-volume trajectories, as in arXiv:2406.17890. The empirical analysis, conducted on hourly cryptocurrency trading data from 80 trading pairs, shows that the globally-fitted model with signature features (GFT-Sig) achieves superior performance in both absolute and quadratic VWAP loss metrics compared to asset-specific approaches. Notably, these improvements persist for out-of-sample assets, demonstrating the model's ability to generalize across different market conditions. The results suggest that combining global parameter sharing with signature-based feature extraction provides a scalable and robust approach to VWAP execution, offering significant practical advantages over traditional asset-specific implementations.